Toward Causally Interpretable Meta-analysis

Dahabreh, Issa J; Petito, Lucia; Robertson, Sarah E.; Hernán, Miguel A.; Steingrimsson, Jon A.

doi:10.1097/ede.0000000000001177

Cited by 51 publications

(52 citation statements)

References 42 publications

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“…After initial screening, a total of 256 articles were screened in full-text for eligibility. Finally, only three distinct methodologies from four publications [6][7][8][9] describing for a causal inference framework in a metaanalysis setting were included in this review (Fig. 1).…”

Section: Systematic Methodology Reviewmentioning

confidence: 99%

“…Dahabreh et al [7,8] proposed a causal inference framework under which meta-analysis estimates are causally interpretable and transportable ATEs to a target population. This approach requires IPD from the randomized trials along with baseline covariate data from a random sample from the target population, in order to account for differences in distributions.…”

Section: Notationmentioning

confidence: 99%

“…Based on the above set of identification conditions, Dahabreh et al [7,8] provided two estimation approaches. The first approach models the conditional ATE directly from the pool of the trials and baseline covariates.…”

Section: Notationmentioning

confidence: 99%

“…The first approach models the conditional ATE directly from the pool of the trials and baseline covariates. The second estimation method is based on a weighting estimator, that is, applying trial-specific weight based on the probability of trial participation and treatment (see [7,8] for details). In both cases, the authors suggest that Wald-type or bootstrap-based confidence intervals can be derived.…”

Section: Notationmentioning

confidence: 99%

“…Since the set of covariates X 2 may include both individual-and study-level covariates, then it seems even more unlikely for A7 to hold unconditionally, since this would mean at least equivalence of the distributions of the individual-level covariates across studies. On the contrary, the approach of Dahabreh et al [7,8] explicitly assumed that the data were obtained by random sampling from an infinite superpopulation (stratified by study S). Dahabreh et al noted that this procedure would lead to a "biased" sample, i.e., the probability of individuals being included in a study differs between the infinite superpopulation and the sampled data, but stated that the identicality of the causal effects is unaffected.…”

Section: Interpretation and Scientific Relevance Of The Identified Causal Effectsmentioning

confidence: 99%

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A survey of methodologies on causal inference methods in meta-analyses of randomized controlled trials

Vourli

Ntzani

2021

Syst Rev

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Background Meta-analyses of randomized controlled trials (RCTs) have been considered as the highest level of evidence in the pyramid of the evidence-based medicine. However, the causal interpretation of such results is seldom studied. Methods We systematically searched for methodologies pertaining to the implementation of a causally explicit framework for meta-analysis of randomized controlled trials and discussed the interpretation and scientific relevance of such causal estimands. We performed a systematic search in four databases to identify relevant methodologies, supplemented with hand-search. We included methodologies that described causality under counterfactuals and potential outcomes framework. Results We only identified three efforts explicitly describing a causal framework on meta-analysis of RCTs. Two approaches required individual participant data, while for the last one, only summary data were required. All three approaches presented a sufficient framework under which a meta-analytical estimate is identifiable and estimable. However, several conceptual limitations remain, mainly in regard to the data generation process under which the selected RCTs rise. Conclusions We undertook a review of methodologies on causal inference methods in meta-analyses. Although all identified methodologies provide valid causal estimates, there are limitations in the assumptions regarding the data generation process and sampling of the potential RCTs to be included in the meta-analysis which pose challenges to the interpretation and scientific relevance of the identified causal effects. Despite both causal inference and meta-analysis being extensively studied in the literature, limited effort exists of combining those two frameworks.

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Section: Systematic Methodology Reviewmentioning

confidence: 99%

Section: Notationmentioning

confidence: 99%

Section: Notationmentioning

confidence: 99%

Section: Notationmentioning

confidence: 99%

Section: Interpretation and Scientific Relevance Of The Identified Causal Effectsmentioning

confidence: 99%

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A survey of methodologies on causal inference methods in meta-analyses of randomized controlled trials

Vourli

Ntzani

2021

Syst Rev

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Comparing Lung Cancer Screening Strategies in a Nationally Representative US Population Using Transportability Methods for the National Lung Cancer Screening Trial

Robertson,

Joyce,

Steingrimsson

et al. 2024

JAMA Netw Open

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ImportanceThe National Lung Screening Trial (NLST) found that screening for lung cancer with low-dose computed tomography (CT) reduced lung cancer–specific and all-cause mortality compared with chest radiography. It is uncertain whether these results apply to a nationally representative target population.ObjectiveTo extend inferences about the effects of lung cancer screening strategies from the NLST to a nationally representative target population of NLST-eligible US adults.Design, Setting, and ParticipantsThis comparative effectiveness study included NLST data from US adults at 33 participating centers enrolled between August 2002 and April 2004 with follow-up through 2009 along with National Health Interview Survey (NHIS) cross-sectional household interview survey data from 2010. Eligible participants were adults aged 55 to 74 years, and were current or former smokers with at least 30 pack-years of smoking (former smokers were required to have quit within the last 15 years). Transportability analyses combined baseline covariate, treatment, and outcome data from the NLST with covariate data from the NHIS and reweighted the trial data to the target population. Data were analyzed from March 2020 to May 2023.InterventionsLow-dose CT or chest radiography screening with a screening assessment at baseline, then yearly for 2 more years.Main Outcomes and MeasuresFor the outcomes of lung-cancer specific and all-cause death, mortality rates, rate differences, and ratios were calculated at a median (25th percentile and 75th percentile) follow-up of 5.5 (5.2-5.9) years for lung cancer–specific mortality and 6.5 (6.1-6.9) years for all-cause mortality.ResultsThe transportability analysis included 51 274 NLST participants and 685 NHIS participants representing the target population (of approximately 5 700 000 individuals after survey-weighting). Compared with the target population, NLST participants were younger (median [25th percentile and 75th percentile] age, 60 [57 to 65] years vs 63 [58 to 67] years), had fewer comorbidities (eg, heart disease, 6551 of 51 274 [12.8%] vs 1 025 951 of 5 739 532 [17.9%]), and were more educated (bachelor’s degree or higher, 16 349 of 51 274 [31.9%] vs 859 812 of 5 739 532 [15.0%]). In the target population, for lung cancer–specific mortality, the estimated relative rate reduction was 18% (95% CI, 1% to 33%) and the estimated absolute rate reduction with low-dose CT vs chest radiography was 71 deaths per 100 000 person-years (95% CI, 4 to 138 deaths per 100 000 person-years); for all-cause mortality the estimated relative rate reduction was 6% (95% CI, −2% to 12%). In the NLST, for lung cancer–specific mortality, the estimated relative rate reduction was 21% (95% CI, 9% to 32%) and the estimated absolute rate reduction was 67 deaths per 100 000 person-years (95% CI, 27 to 106 deaths per 100 000 person-years); for all-cause mortality, the estimated relative rate reduction was 7% (95% CI, 0% to 12%).Conclusions and RelevanceEstimates of the comparative effectiveness of low-dose CT screening compared with chest radiography in a nationally representative target population were similar to those from unweighted NLST analyses, particularly on the relative scale. Increased uncertainty around effect estimates for the target population reflects large differences in the observed characteristics of trial participants and the target population.

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Causally interpretable meta‐analysis: Clearly defined causal effects and two case studies

Rott,

Bronfort,

Chu

et al. 2023

Research Synthesis Methods

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Meta‐analysis is commonly used to combine results from multiple clinical trials, but traditional meta‐analysis methods do not refer explicitly to a population of individuals to whom the results apply and it is not clear how to use their results to assess a treatment's effect for a population of interest. We describe recently‐introduced causally interpretable meta‐analysis methods and apply their treatment effect estimators to two individual‐participant data sets. These estimators transport estimated treatment effects from studies in the meta‐analysis to a specified target population using the individuals' potentially effect‐modifying covariates. We consider different regression and weighting methods within this approach and compare the results to traditional aggregated‐data meta‐analysis methods. In our applications, certain versions of the causally interpretable methods performed somewhat better than the traditional methods, but the latter generally did well. The causally interpretable methods offer the most promise when covariates modify treatment effects and our results suggest that traditional methods work well when there is little effect heterogeneity. The causally interpretable approach gives meta‐analysis an appealing theoretical framework by relating an estimator directly to a specific population and lays a solid foundation for future developments.

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Toward Causally Interpretable Meta-analysis

Cited by 51 publications

References 42 publications

A survey of methodologies on causal inference methods in meta-analyses of randomized controlled trials

A survey of methodologies on causal inference methods in meta-analyses of randomized controlled trials

Comparing Lung Cancer Screening Strategies in a Nationally Representative US Population Using Transportability Methods for the National Lung Cancer Screening Trial

Causally interpretable meta‐analysis: Clearly defined causal effects and two case studies

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